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Biomedical subjects

S G Yuan

Publications and source records attributed to S G Yuan.

6 recordsLinked to original sources
PubMed2004

CISOC-PSCT: a predictive system for carcinogenic toxicity.

A SAR based carcinogenic toxicity prediction system, CISOC-PSCT, was developed. It consisted of two principal phases: the construction of relationships between structural descriptors and carcinogenic toxicity indices, and prediction of the toxicity from the SAR model. The training set included 2738 carcinogenic and 4130 non-carcinogenic compounds. Three predefined topological types of substructures termed Star, Path and Ring were used to generate the descriptors for each structure in the training set. In this system, the defined carcinogenic toxicity index (CTI) was obtained from the probability of a structural descriptor to either belong to the carcinogenic or non-carcinogenic compounds. Based on these structural descriptors and their CTI, a SAR model was derived. Then the carcinogenic possibility (CP) and the carcinogenic impossibility (CIP) of compounds were predicted. The model was tested from a testing set of 304 carcinogenic compounds (MDL toxicity database), 460 non-carcinogenic compounds (CMC database) and 94 compounds extracted from two traditional Chinese medicine herbs.

Carcinogens↗
PubMed2003

Virtual screening and rational drug design method using structure generation system based on 3D-QSAR and docking.

An efficient virtual and rational drug design method is presented. It combines virtual bioactive compound generation with 3D-QSAR model and docking. Using this method, it is possible to generate a lot of highly diverse molecules and find virtual active lead compounds. The method was validated by the study of a set of anti-tumor drugs. With the constraints of pharmacophore obtained by DISCO implemented in SYBYL 6.8, 97 virtual bioactive compounds were generated, and their anti-tumor activities were predicted by CoMFA. Eight structures with high activity were selected and screened by the 3D-QSAR model. The most active generated structure was further investigated by modifying its structure in order to increase the activity. A comparative docking study with telomeric receptor was carried out, and the results showed that the generated structures could form more stable complexes with receptor than the reference compound selected from experimental data. This investigation showed that the proposed method was a feasible way for rational drug design with high screening efficiency.

Colchicine↗
PubMed2000

[Effects on ang II receptor antagonist on experimental glomerulosclerosis].

OBJECTIVE: To observe the effects of angiotensin II receptor antagonist losartan in experimental glomerulosclerosis. METHODS: The 5/6 nephrectomized rats were randomly divided into losartan treatment group and control group, the rats with sham operation served as normal control. Urine proteins were measured in the 2nd, 4th and 6th week after operation, and serum BUN, creatinine, total protein and albumin were measured in the 6th week following operation. Renal pathologic changes were evaluated in the 6th week. RESULTS: Losartan not only reduced urine protein, serum creatinine and BUN(P < 0.01), but also significantly ameliorated glomerular mesangial proliferation and glomerular sclerosis. CONCLUSION: The results suggest that losartan can retard progression of glomerulosclerosis in 5/6 nephrectomized rats.

Angiotensin Receptor Antagonists↗
PubMedDate not supplied

Comparative study of non nucleoside inhibitors with HIV-1 reverse transcriptase based on 3D-QSAR and docking.

The intermolecular interaction between two types of non nucleoside reverse transcriptase inhibitors (NNRTIs), HEPT and TIBO, and HIV reverse transcriptase receptor (HIVRT) was investigated. The result of docking study showed that two types of NNRTIs presented similar interaction mechanism with HIVRT. The most active compound of every type of inhibitors could form one hydrogen bond with the residue Lys101 and has hydrophobic interaction with residues Tyr181, Tyr188 and Tyr318, etc. Three 3D-QSAR models including two partial correlation models (one for each family of HEPT and TIBO) and a mixed model gathering two families were constructed. Comparative study of these models indicated that the mixed model offered the strongest prediction ability. For this model, the cross-validated q2 values were 0.720 and 0.675, non-cross-validated r2 values were 0.940 and 0.920 for CoMFA and CoMSIA, respectively. It has been validated by using a test set of 27 inhibitors. Compared with previously reported works, our model showed better prediction ability. It could help us to insight the interaction between NNRTIs and HIVRT, and to design new anti-HIV NNRTIs inhibitors.

Drug Interactions↗